Ontology-based Conversational Recommender System for Recommending Camera

Abstract

The camera is a product that has developed very quickly in terms of specifications and functions. In addition, the cameras available on the market are becoming increasingly varied, so customers need more time to find a camera that suits their needs. Currently, many recommender systems have been developed to assist users in finding suitable products, especially the conversational recommender system (CRS). CRS is a recommender system that recommends products through conversations between the user and the system. However, many developed CRS still forces users to have knowledge of the product's technical characteristics. In the real world, many people are not familiar with the technical features of products, especially cameras. People interact more easily with CRS by stating the camera function they want. In this study, we call that statement functional requirements. Therefore, we proposed a CRS for recommending cameras that interact with users using functional requirements. This CRS uses semantic reasoning techniques on ontologies. To evaluate system performance, we use two parameters, i.e., user satisfaction and recommendation accuracy. The evaluation results show that the accuracy of the recommendations is at a value of 82.35%, and the level of user satisfaction reaches 0.66. With these results, the system can provide recommendations accurately and satisfy users.